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All Journal SMATIKA KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Informatika Terpadu Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Jurnal Ilmu Komputer Ruru
Ruli Herdiana
STMIK IKMI Cirebon
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Journal : Jurnal Ilmu Komputer Ruru

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PREDIKSI PENETAPAN TARIF PENERBANGAN MENGGUNAKAN AUTO-ML DENGAN ALGORITMA RANDOM FOREST Ruli Herdiana
Jurnal Ilmu Komputer Ruru Vol. 2 No. 1 (2025): Edisi Januari
Publisher : Yayasan Grace Berkat Anugerah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69688/jikr.v2i1.10

Abstract

With so many airlines competing with each other, airlines are competing to become the consumer/market's main choice, but to achieve this, there is no airline strategy that can predict the price of airline tickets according to market needs. To meet the needs of airlines, we need a way to determine the price of airline tickets according to market needs with the help of the influence of technology and information. This research method was carried out using Google Collaboratory as a media to create a data model with the Random Forest, Logistic Regression and Gradient Boosting Regressor algorithms. In this study, the model that produced the highest R2 value and the lowest RMSE was a random forest with an R2 value of 83.91% and an RMSE of $175.9. However, from the three models, Random Forest got a change in accuracy of 1.96% to 85.87. To assist in predicting the determination of flight fares, airline companies can more easily and be alert to determine flight fares that are in accordance with the market. Therefore, Random Forest can be declared better than Logistic Regression and Gradient Boosting models. The Random Forest model that has been created can be used to predict in real-time using Machine Learning.
Co-Authors Abdul Rasyid Faiq Adinata Cep Lukman Rohmat Chaidir Ali Iin Irma Sumiati Isni Rinjani Muhamad Sulaiman Muhammad Harun Al- Musa Narasati, Riri Narasati Nisa Dieanwati Nuris Nova Novitasari Odi Nurdiawan Rahayu Arumsari Riandy Yudistira Saeful Anwar Saeful Anwar Sandy Eka Permana Umi Hayati